How analytics helped hospitals cope with Covid surge

Modelling software played key role as NHS Lanarkshire prepared for pandemic


Health systems experts worked with a Scottish health board and a software company to better predict the local critical care demands caused by the Covid-19 pandemic. NHS Lanarkshire had been

advised by central government to prepare for a five-fold increase in demand for intensive care beds within its hospitals as the corona- virus peaked early in April. But academics at the University

of Strathclyde Business School worked with the board and soft- ware modellers Simul8 to devise a model that showed they had already made sufficient updates to its capacity to cope with the surge. Tis meant avoiding the costly adaptations to resourcing needs that would have otherwise been wasteful, as well as provid- ing front-line staff and capacity planners with peace of mind. Dr Nicola Irvine, consultant

physician, doctoral researcher and one of the team leads, said: “Once the executive team at Lanarkshire had set their key question – which was what will be your critical care need and do we currently have the resource and the capability to meet that? – the fact that we were able to give them the answer within two weeks, and roughly seven to 10 days before the peak started, was vital in helping them manage this pandemic.”

Te model has now been

optimised to support the de- velopment of an early warning system for the next stage in the Covid-19 pandemic, covering the three main university hospitals, Wishaw, Hairmyres and Monk- lands. Chandrava Sinha from the

Department of Management Sci- ence at the University of Strath- clyde, who worked with Irvine and Gillian Anderson Research Associate from the university team, in building the simulation model, said: “A digital model is an approximate representation of any real-life system. Tey are ba- sically mathematical or statistical models created using a computer which tries to best mimic and present a real-life scenario or a proposed scenario, and to then answer various ‘what if’ questions to help decision-makers make a very well-informed decision.”

A crucial element of the modelling process for NHS La- narkshire was the use of data that the team were able to build into the simulation. To cut through any conflicting evidence and to

24 | FUTURESCOT | WINTER 2020/21

make the model as accurate to local needs as possible, the team drew on a range of data sets. Tis included very localised com- munity data, such as population profiling, as well as national trends that were being received from central government. It also included wider international data from countries such as Italy and Spain where the pandemic wave was a few weeks ahead. Tis approach allowed Simul8 to create a model that was as close as possible to the local situation. Chandrava added: “Tis data

all fed into the model and then gave us the maximum utilisa- tion of beds across all different categories on a week-by-week basis for the whole first wave of the pandemic.” Irvine emphasised the need

for a “triumvirate of executive expertise, clinical expertise and modelling expertise” in building and implementing a successful model such as this one. Te clinician understands the

behaviours of the organisation at floor level; the modeller is able to interpret that nuanced dynamic environment and to simplify and

Health secretary Jeane Freeman (right) and national clinical director Jason Leitch on a visit to Hairmyres, one of the three NHS Lanarkshire hospitals where the Simul8 analysis took place

abstract data into a model that can be usefully predictive; and an executive team will have the overview needed to set the most pertinent question, and then the authority to act on the predic- tions of the model. “Validation is also a key part

of any modelling process,” Dr Irvine said. “You want to make sure that you’ve captured the process that you are modelling, the environment, the disease, the activity, etc. Crucial to this was the daily information that we were receiving from the hospital’s management team. We were able to constantly update our simulation using data from the local hospitals and au- thorities, as well as from wider resources such as the intensive care audit and information from the European Centre for Disease Control.”l

Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68